Protein-Protein Interactions

Targeting PPI for Therapeutic Interventions

April 17-18, 2013

Cambridge Healthtech Institute's conference on Protein-Protein Interactions has established itself as a successful and well attended event year after year. Now in its sixth year, this conference is addressing current challenges in PPI such as interaction with binding sites, modulation of PPI, predicting PPI modes and what can be done to target PPI successfully.

To have an impact, interventions within a PPI network need to be multiple but highly selective. The major challenge is to design a promiscuous strategy that hits multiple weak nodes in cancer cell PPI without invoking undesirable side effects to normal cell network. The emergence of systems and network biology has enhanced our knowledge of PPIs and has allowed deeper evaluations of drug induced perturbations that has helped to decode the complex mechanisms of drug action. Emerging concepts such as ‘Network Pharmacology’ and ‘Systems Pharmacology’ are solidifying their position in cancer medicine.

2:10 PPI Drug Discovery - Peptide Mimicry and Fragment Approaches

David Fry, Ph.D., Head, Biostructural Research, Hoffman-La Roche

Modulating protein-protein interactions (PPIs) with small molecules is a difficult objective, but could potentially lead to a wide variety of novel and important therapeutics. PPI systems represent a unique class of drug target, and it has been shown that successful modulators of PPIs tend to have certain properties that distinguish them from drugs that act against more conventional target classes. One way toward understanding these key properties is to carefully study successful examples of PPI modulators and, at an atomic level, compare their binding strategies to those employed by the natural protein partners. Further, with regard to the fragment-based approach, we can learn by performing retrospective analyses of completed, successful programs - that is, deconstruct known PPI modulators into successively smaller fragments, and survey their potency and binding locations, and then compare these attributes to those of the parent compounds.

Since the original report of BiFC in 2002, it has become a widely accepted method to study PPIs in various model organisms. Many critical PPIs have also been identified by the use of BiFC in living cells and animals. Further, recent improvements in the technology have also increased signal-to-noise ratio dramatically. Compared to other methods such as FRET, its higher signal-to-noise ratio (20 fold) is the most attractive feature for BiFC-based high throughput screening.

Recent advances in computational methods have improved the predictive capabilities of modeling antibodies and protein-protein interaction energies. Here, we present recent work aimed at improving the speed and accuracy of antibody hypervariable loop prediction, and show high quality models can be generated for a large number of antibodies. In addition, we show that a more computationally intensive physics-based method is able to achieve a high degree of accuracy on the challenging H3 loop. Finally, we present results from a recent study on computational residue scanning to detect residue mutations at a protein-protein interface that contribute to significant favorable or unfavorable changes in binding energy.

David Koes, Ph.D., Research Assistant Professor, Computational and Systems Biology, University of Pittsburgh

PocketQuery is a web interface for exploring the properties of protein-protein interaction (PPI) interfaces with a focus on the discovery of promising starting points for small-molecule design. PocketQuery rapidly focuses attention on the key interacting residues of an interaction using a ‘druggability’ score that provides an estimate of how likely the chemical mimicry of a cluster of interface residues would result in a small-molecule inhibitor of an interaction. These residue clusters are chemical starting points that can be seamlessly exported to a pharmacophore-based drug discovery workflow.

4:50 Recent Advances in the Prediction of Protein Interaction Interfaces

Computational methods to predict interaction sites using protein structure and sequence information are coming out of age. Recent developments in this field, accuracy of current prediction methods, inherent limitations and challenges are presented. Prediction of hot spots and druggable sites within interaction interfaces are also discussed.

5:20 - 6:20 Moderated Breakout Discussions

In this interactive session, several topics will be offered for discussions and delegates are invited to choose a topic of interest and join the moderated discussion at hand. In this informal setting, participants are encouraged to share examples from their work, vet ideas with peers and be part of a group problem-solving endeavor. We emphasize that this is an informal exchange amongst scientists and is not meant to be, in any way, a product promoting session.